Digitizing Omics Profiles by Divergence from a Baseline

In this study we develop a method enabling the analysis of omics profiles in the same way one can do with a clinical test. This is particularly important since technological advances have enabled the comprehensive profiling of cellular molecular landscapes, which can inform personalized treatment of complex diseases. Two major obstacles are the complexity of these data and the high degree of person-to-person heterogeneity. We develop a highly simplified, personalized data representation by comparing the profile of an individual to the range of landscapes in a baseline population, thereby mimicking basic clinical diagnostic testing for departures of selected variables from normal levels. Moreover, our method can be applied to any data modality and at any level of granularity, from single features to any subset of features treated as a single entity, for example the gene expression levels in a pathway. Experiments involve both healthy human tissues and various cancer subtypes.